Introduction

Due to the non-stationary nature of the environment of the financial market, the data distribution may change in different periods, which makes the performance of models build on training data decays in the future test data. So adapting the forecasting models/strategies to market dynamics is very important to the model/strategies' performance.

The table below shows the performances of different solutions on different forecasting models.

Alpha158 Dataset

Here is the crowd sourced version of qlib data: https://github.com/chenditc/investment_data/releases

bash

wget https://github.com/chenditc/investment_data/releases/latest/download/qlib_bin.tar.gz
mkdir -p ~/.qlib/qlib_data/cn_data
tar -zxvf qlib_bin.tar.gz -C ~/.qlib/qlib_data/cn_data --strip-components=2
rm -f qlib_bin.tar.gz

Model Name	Dataset	IC	ICIR	Rank IC	Rank ICIR	Annualized Return	Information Ratio	Max Drawdown
RR[Linear]	Alpha158	0.0945	0.5989	0.1069	0.6495	0.0857	1.3682	-0.0986
DDG-DA[Linear]	Alpha158	0.0983	0.6157	0.1108	0.6646	0.0764	1.1904	-0.0769
RR[LightGBM]	Alpha158	0.0816	0.5887	0.0912	0.6263	0.0771	1.3196	-0.0909
DDG-DA[LightGBM]	Alpha158	0.0878	0.6185	0.0975	0.6524	0.1261	2.0096	-0.0744

The label horizon of the Alpha158 dataset is set to 20.
The rolling time intervals are set to 20 trading days.
The test rolling periods are from January 2017 to August 2020.
The results are based on the crowd-sourced version. The Yahoo version of qlib data does not contain VWAP, so all related factors are missing and filled with 0, which leads to a rank-deficient matrix (a matrix does not have full rank) and makes lower-level optimization of DDG-DA can not be solved.